## TAO Pretrained EfficientDet

### What is Train Adapt Optimize (TAO) Toolkit? <a name="introduction" class="anchor"></a>

[Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit) is a python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune and export highly optimized and accurate AI models for edge deployment.

The pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.

Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO, [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). The models are suitable for object detection, classification and segmentation.

### Model Overview <a class="anchor" name="model overview"></a>

Object detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and places bounding boxes around the object. This model card contains pre-trained weights for the backbones that may be used as a starting point with the [EfficientDet](https://arxiv.org/pdf/1911.09070.pdf) object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.

### Model Architecture <a class="anchor" name="efficientdet_based_object_detection"></a>

The models in this instance are feature extractors based on the [EfficientNet](https://arxiv.org/pdf/1905.11946.pdf) architecture.

### Training

#### Dataset

These models are trained on a subset of the Google OpenImages dataset.

### Performance

No performance data is available with this model.

### How to Use this Model

#### Running EfficientDet Models Using TAO <a class="anchor" name="running_object_detection_models_using_tao"></a>

The EfficientDet app in TAO expect data in COCO format. TAO provides a simple command line interface to train a deep learning model for object detection.

The models in this model area are only compatible with TAO Toolkit. For more information about the TAO container, please visit the TAO container page.

1. Install the NGC CLI from ngc.nvidia.com

2. Configure the NGC CLI using the following command

```sh
ngc config set
```

3. To view all the backbones that are supported by object detection architecture in TAO:

```sh
ngc registry model list nvidia/tao/pretrained_efficientdet:*
```

4. To download the model:

```sh
ngc registry model download-version nvidia/tao/pretrained_efficientdet:<template> --dest <path>
```

#### Instructions to run the sample notebook

1. Get the NGC API key from the SETUP tab on the left. Please store this key for future use. Detailed instructions can be found [here](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html#generating-api-key)

2. Configure the NGC command line interface using the command mentioned below and follow the prompts.

```sh
ngc config set
```

3. Download the sample notebooks from NGC using the command below

```sh
ngc registry resource download-version "nvidia/tao/cv_samples:v1.3.0"
```

4. Invoke the jupyter notebook using the following command

```sh
jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
```

5. Open an internet browser and type in the following URL to start running the notebooks when running on a local machine.

```sh
http://0.0.0.0:8888
```

If you wish to run view the notebook from a remote client, please modify the URL as follows:

```sh
http://a.b.c.d:8888
```

Where, the `a.b.c.d` is the IP address of the machine running the container.

### Limitations

These are unpruned models with just the feature extractor weights and, may not be used without re-training in an EfficientDet object detection application with TAO Toolkit.

### Versions

The following efficientnet-x backbone versions are supported in TAO Toolkit:

- efficientnet-b0
- efficientnet-b1
- efficientnet-b2
- efficientnet-b3
- efficientnet-b4
- efficientnet-b5

### License <a class="anchor" name="license"></a>

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, please visit this [link](http://creativecommons.org/licenses/by/4.0/),  or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

### Technical blogs <a class="anchor" name="technical_blogs"></a>

- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)
- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)
- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)
- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)
- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)
- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1)  |  [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)
- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.
- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO

### Suggested reading <a class="anchor" name="suggested_reading"></a>

- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)
- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)
- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).
- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)
- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)
- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.

### Ethical Considerations <a class="anchor" name="ethical_ai"></a>

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.


pretrained_efficientdet

Pretrained weights to facilitate transfer learning using TAO Toolkit.

TAO Pretrained EfficientDet

Description

Publisher

Latest Version

Modified

Size